System and method for vehicle taillight state recognition

ABSTRACT

A system and method for taillight signal recognition using a convolutional neural network is disclosed. An example embodiment includes: receiving a plurality of image frames from one or more image-generating devices of an autonomous vehicle; using a single-frame taillight illumination status annotation dataset and a single-frame taillight mask dataset to recognize a taillight illumination status of a proximate vehicle identified in an image frame of the plurality of image frames, the single-frame taillight illumination status annotation dataset including one or more taillight illumination status conditions of a right or left vehicle taillight signal, the single-frame taillight mask dataset including annotations to isolate a taillight region of a vehicle; and using a multi-frame taillight illumination status dataset to recognize a taillight illumination status of the proximate vehicle in multiple image frames of the plurality of image frames, the multiple image frames being in temporal succession.

PRIORITY PATENT APPLICATION

This patent document claims the benefit of U.S. patent application Ser.No. 16/542,770, filed on Aug. 16, 2019, and U.S. patent application Ser.No. 15/709,832, filed on Sep. 20, 2017, which are incorporated herein byreference in its entirety for all purposes.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2016-2020, TuSimple, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatus,methodologies, computer program products, etc.) for image processing,vehicle control systems, and autonomous driving systems, and moreparticularly, but not by way of limitation, to a system and method fordetecting taillight signals of a vehicle.

BACKGROUND

For all motor vehicles operated on public roadways, taillight signalsare a legally required item. The status of the taillight signals canhelp drivers understand an intention of another driver in a vehicle infront (a leading proximate vehicle). For an autonomous vehicle controlsystem, it is crucial to identify the status of the taillight signals ofa vehicle and thereby determine the intentions of the drivers of otherleading vehicles. Additionally, there are limitations in theconventional camera systems in autonomous vehicles, which make itdifficult for conventional autonomous vehicle control systems torecognize the status of the taillights of leading vehicles. Moreover, itis more complex for autonomous vehicle control systems to distinguish ataillight signal indicating a turning intention than a taillight signalindicating a braking condition. The diversity of vehicle types alsoposes many challenges, especially considering heavy-duty vehicles.Conventional autonomous vehicle control systems have been unable toimplement a taillight recognition capability that replicates a humandriver's ability to quickly and accurately recognize taillight signalsin a variety of driving conditions. As a result, the safety andefficiency of autonomous vehicle control is being compromised by theinability of conventional systems to implement taillight recognition fordetermining the intentions of drivers of leading proximate vehicles.

SUMMARY

A system and method for detecting taillight signals of a vehicle aredisclosed. Taillight recognition is the task of detecting vehicletaillight signals, including brake, turn, and emergency stop signals. Invarious example embodiments disclosed herein, a taillight signalrecognition system is provided. An example embodiment can automaticallydetect taillight signals for all types of vehicles in real time and inall driving conditions. The example embodiment can use front-facingcameras mounted on the subject vehicle as input sensors. The exampleembodiments provide a system and method for automatically detectingtaillight signals of a proximate leading vehicle, which includesreceiving, at a computing device, a sequence of images from one or morecameras of a subject vehicle, generating a frame for each of the images,and labelling the images with one of three states of the taillightsignals of proximate leading vehicles. The method further includescreating a first and a second dataset corresponding to the images andtraining a convolutional neural network to combine the first and seconddataset. The method includes identifying a confidence levelcorresponding to statistics of temporal patterns of taillight signals,loading the confidence level to a calculating model, and refiningparameters of the calculating model.

The taillight signal recognition system of the example embodiments canbe implemented by generating datasets and machine learning models torecognize and act upon the taillight illumination status of proximatevehicles (e.g., vehicles near an autonomous vehicle). In particular, anexample embodiment can be implemented by: 1) creating a trajectory levelfully human-annotated dataset for taillight state recognition; 2)creating a deep learning based feature extractor for taillight maskfeature extraction; and 3) creating a machine learning based model foraccurate trajectory level taillight state recognition. In the disclosureherein, the term trajectory level refers to the capture and processingof taillight illumination status of proximate vehicles over multipleimage frames in temporal succession as each proximate vehicle moves inits trajectory. The creation and use of these datasets and machinelearning models for example embodiments are described in more detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which anin-vehicle taillight signal recognition system of an example embodimentcan be implemented;

FIG. 2 illustrates the components of the taillight signal recognitionsystem of an example embodiment;

FIG. 3 is a basic flow diagram illustrating an example embodiment of asystem and method for detecting taillight signals of a vehicle;

FIGS. 4 through 6 illustrate an example of the processing performed bythe taillight signal recognition module of an example embodiment;

FIG. 7 is a detailed flow diagram illustrating an example embodiment ofa system and method for detecting taillight signals of a vehicle;

FIG. 8 illustrates the datasets used in an example embodiment of thetaillight signal recognition system;

FIG. 9 illustrates an example of a taillight mask annotation for aparticular proximate vehicle;

FIG. 10 illustrates the taillight signal recognition operationsperformed by the taillight signal recognition module of an exampleembodiment;

FIGS. 11 and 12 illustrate examples of multiple image frame sets intemporal succession and the predicted or recognized taillightillumination state for each image frame;

FIG. 13 is a process flow diagram illustrating an example embodiment ofa system and method for detecting taillight signals of a vehicle; and

FIG. 14 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method fordetecting taillight signals of a vehicle are described herein. Anexample embodiment disclosed herein can be used in the context of anin-vehicle control system 150 in a vehicle ecosystem 101. In one exampleembodiment, an in-vehicle control system 150 with a taillight signalrecognition module 200 resident in a vehicle 105 can be configured likethe architecture and ecosystem 101 illustrated in FIG. 1. However, itwill be apparent to those of ordinary skill in the art that thetaillight signal recognition module 200 described and claimed herein canbe implemented, configured, and used in a variety of other applicationsand systems as well.

Referring now to FIG. 1, a block diagram illustrates an exampleecosystem 101 in which an in-vehicle control system 150 and a taillightsignal recognition module 200 of an example embodiment can beimplemented. These components are described in more detail below.Ecosystem 101 includes a variety of systems and components that cangenerate and/or deliver one or more sources of information/data andrelated services to the in-vehicle control system 150 and the taillightsignal recognition module 200, which can be installed in the vehicle105. For example, a camera (or other image-generating device) installedin the vehicle 105, as one of the devices of vehicle subsystems 140, cangenerate image and timing data that can be received by the in-vehiclecontrol system 150. The in-vehicle control system 150 and the taillightsignal recognition module 200 executing therein can receive this imageand timing data input. As described in more detail below, the taillightsignal recognition module 200 can process the image input and generatetaillight signal status information, which can be used by an autonomousvehicle control subsystem, as another one of the subsystems of vehiclesubsystems 140. The autonomous vehicle control subsystem, for example,can use the real-time generated taillight signal status information tosafely and efficiently navigate and control the vehicle 105 through areal world driving environment while avoiding obstacles and safelycontrolling the vehicle.

In an example embodiment as described herein, the in-vehicle controlsystem 150 can be in data communication with a plurality of vehiclesubsystems 140, all of which can be resident in a user's vehicle 105. Avehicle subsystem interface 141 is provided to facilitate datacommunication between the in-vehicle control system 150 and theplurality of vehicle subsystems 140. The in-vehicle control system 150can be configured to include a data processor 171 to execute thetaillight signal recognition module 200 for processing image datareceived from one or more of the vehicle subsystems 140. The dataprocessor 171 can be combined with a data storage device 172 as part ofa computing system 170 in the in-vehicle control system 150. The datastorage device 172 can be used to store data, processing parameters, anddata processing instructions. A processing module interface 165 can beprovided to facilitate data communications between the data processor171 and the taillight signal recognition module 200. In various exampleembodiments, a plurality of processing modules, configured similarly totaillight signal recognition module 200, can be provided for executionby data processor 171. As shown by the dashed lines in FIG. 1, thetaillight signal recognition module 200 can be integrated into thein-vehicle control system 150, optionally downloaded to the in-vehiclecontrol system 150, or deployed separately from the in-vehicle controlsystem 150.

The in-vehicle control system 150 can be configured to receive ortransmit data from/to a wide-area network 120 and network resources 122connected thereto. An in-vehicle web-enabled device 130 and/or a usermobile device 132 can be used to communicate via network 120. Aweb-enabled device interface 131 can be used by the in-vehicle controlsystem 150 to facilitate data communication between the in-vehiclecontrol system 150 and the network 120 via the in-vehicle web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe in-vehicle control system 150 to facilitate data communicationbetween the in-vehicle control system 150 and the network 120 via theuser mobile device 132. In this manner, the in-vehicle control system150 can obtain real-time access to network resources 122 via network120. The network resources 122 can be used to obtain processing modulesfor execution by data processor 171, data content to train internalneural networks, system parameters, or other data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via in-vehicle web-enableddevices 130 or user mobile devices 132. The network resources 122 canalso host network cloud services, which can support the functionalityused to compute or assist in processing image input or image inputanalysis. Antennas can serve to connect the in-vehicle control system150 and the taillight signal recognition module 200 with the datanetwork 120 via cellular, satellite, radio, or other conventional signalreception mechanisms. Such cellular data networks are currentlyavailable (e.g., Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-baseddata or content networks are also currently available (e.g., SiriusXM™,HughesNet™, etc.). The conventional broadcast networks, such as AM/FMradio networks, pager networks, UHF networks, gaming networks, WiFinetworks, peer-to-peer networks, Voice over IP (VoIP) networks, and thelike are also well-known. Thus, as described in more detail below, thein-vehicle control system 150 and the taillight signal recognitionmodule 200 can receive web-based data or content via an in-vehicleweb-enabled device interface 131, which can be used to connect with thein-vehicle web-enabled device receiver 130 and network 120. In thismanner, the in-vehicle control system 150 and the taillight signalrecognition module 200 can support a variety of network-connectablein-vehicle devices and systems from within a vehicle 105.

As shown in FIG. 1, the in-vehicle control system 150 and the taillightsignal recognition module 200 can also receive data, image processingcontrol parameters, and training content from user mobile devices 132,which can be located inside or proximately to the vehicle 105. The usermobile devices 132 can represent standard mobile devices, such ascellular phones, smartphones, personal digital assistants (PDA's), MP3players, tablet computing devices (e.g., iPad™), laptop computers, CDplayers, and other mobile devices, which can produce, receive, and/ordeliver data, image processing control parameters, and content for thein-vehicle control system 150 and the taillight signal recognitionmodule 200. As shown in FIG. 1, the mobile devices 132 can also be indata communication with the network cloud 120. The mobile devices 132can source data and content from internal memory components of themobile devices 132 themselves or from network resources 122 via network120. Additionally, mobile devices 132 can themselves include a GPS datareceiver, accelerometers, WiFi triangulation, or other geo-locationsensors or components in the mobile device, which can be used todetermine the real-time geo-location of the user (via the mobile device)at any moment in time. In any case, the in-vehicle control system 150and the taillight signal recognition module 200 can receive data fromthe mobile devices 132 as shown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the in-vehicle controlsystem 150 via vehicle subsystem interface 141 may include informationabout the state of one or more of the components or subsystems of thevehicle 105. In particular, the data signals, which can be communicatedfrom the vehicle operational subsystems 140 to a Controller Area Network(CAN) bus of the vehicle 105, can be received and processed by thein-vehicle control system 150 via vehicle subsystem interface 141.Embodiments of the systems and methods described herein can be used withsubstantially any mechanized system that uses a CAN bus or similar datacommunications bus as defined herein, including, but not limited to,industrial equipment, boats, trucks, machinery, or automobiles; thus,the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the in-vehicle control system 150, thecomputing system 170, and the taillight signal recognition module 200.The vehicle 105 may include more or fewer subsystems and each subsystemcould include multiple elements. Further, each of the subsystems andelements of vehicle 105 could be interconnected. Thus, one or more ofthe described functions of the vehicle 105 may be divided up intoadditional functional or physical components or combined into fewerfunctional or physical components. In some further examples, additionalfunctional and physical components may be added to the examplesillustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit, and one ormore cameras or other image-generating devices. The vehicle sensorsubsystem 144 may also include sensors configured to monitor internalsystems of the vehicle 105 (e.g., an O2 monitor, a fuel gauge, an engineoil temperature). Other sensors are possible as well. One or more of thesensors included in the vehicle sensor subsystem 144 may be configuredto be actuated separately or collectively in order to modify a position,an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit may be anysensor configured to sense objects in the environment in which thevehicle 105 is located using lasers. In an example embodiment, the laserrange finder/LIDAR unit may include one or more laser sources, a laserscanner, and one or more detectors, among other system components. Thelaser range finder/LIDAR unit could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode. The cameras may include one or more devices configured to capturea plurality of images of the environment of the vehicle 105. The camerasmay be still image cameras or motion video cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the taillight signal recognition module 200, the GPS transceiver,and one or more predetermined maps so as to determine the driving pathfor the vehicle 105. The autonomous control unit may represent a controlsystem configured to identify, evaluate, and avoid or otherwisenegotiate potential obstacles in the environment of the vehicle 105. Ingeneral, the autonomous control unit may be configured to control thevehicle 105 for operation without a driver or to provide driverassistance in controlling the vehicle 105. In some embodiments, theautonomous control unit may be configured to incorporate data from thetaillight signal recognition module 200, the GPS transceiver, the RADAR,the LIDAR, the cameras, and other vehicle subsystems to determine thedriving path or trajectory for the vehicle 105. The vehicle controlsystem 146 may additionally or alternatively include components otherthan those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween vehicle 105 and external sensors, other vehicles, other computersystems, and/or an occupant or user of vehicle 105. For example, theoccupant interface subsystems 148 may include standard visual displaydevices (e.g., plasma displays, liquid crystal displays (LCDs),touchscreen displays, heads-up displays, or the like), speakers or otheraudio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 142, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as image processing parameters, training data,roadway maps, and path information, among other information. Suchinformation may be used by the vehicle 105 and the computing system 170during the operation of the vehicle 105 in the autonomous,semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 142, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by vehicle sensor subsystem 144 and thetaillight signal recognition module 200, move in a controlled manner, orfollow a path or trajectory based on output generated by the taillightsignal recognition module 200. In an example embodiment, the computingsystem 170 can be operable to provide control over many aspects of thevehicle 105 and its subsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, andtaillight signal recognition module 200, as being integrated into thevehicle 105, one or more of these components could be mounted orassociated separately from the vehicle 105. For example, data storagedevice 172 could, in part or in full, exist separate from the vehicle105. Thus, the vehicle 105 could be provided in the form of deviceelements that may be located separately or together. The device elementsthat make up vehicle 105 could be communicatively coupled together in awired or wireless fashion.

Additionally, other data and/or content (denoted herein as ancillarydata) can be obtained from local and/or remote sources by the in-vehiclecontrol system 150 as described above. The ancillary data can be used toaugment, modify, or train the operation of the taillight signalrecognition module 200 based on a variety of factors including, thecontext in which the user is operating the vehicle (e.g., the locationof the vehicle, the specified destination, direction of travel, speed,the time of day, the status of the vehicle, etc.), and a variety ofother data obtainable from the variety of sources, local and remote, asdescribed herein.

In a particular embodiment, the in-vehicle control system 150 and thetaillight signal recognition module 200 can be implemented as in-vehiclecomponents of vehicle 105. In various example embodiments, thein-vehicle control system 150 and the taillight signal recognitionmodule 200 in data communication therewith can be implemented asintegrated components or as separate components. In an exampleembodiment, the software components of the in-vehicle control system 150and/or the taillight signal recognition module 200 can be dynamicallyupgraded, modified, and/or augmented by use of the data connection withthe mobile devices 132 and/or the network resources 122 via network 120.The in-vehicle control system 150 can periodically query a mobile device132 or a network resource 122 for updates or updates can be pushed tothe in-vehicle control system 150.

Referring now to FIG. 2, a diagram illustrates the components of thetaillight signal recognition system 201, with the taillight signalrecognition module 200 therein, of an example embodiment. In the exampleembodiment, the taillight signal recognition module 200 can beconfigured to include a deep convolutional neural network 173 and ataillight signal status determination module 175. As described in moredetail below, the deep convolutional neural network 173 and thetaillight signal status determination module 175 serve to analyze one ormore input images 210 received from one or more of the vehicle sensorsubsystems 144, including one or more cameras, and determine the statusof taillight signals corresponding to the images of proximate leadingvehicles. The deep convolutional neural network 173 and taillight signalstatus determination module 175 can be configured as software modulesexecuted by the data processor 171 of the in-vehicle control system 150.The modules 173 and 175 of the taillight signal recognition module 200can receive the image input 210 and produce taillight signal statusinformation 220 as described in more detail below. As a result, theautonomous control subsystem of the vehicle control subsystem 146 canmore efficiently and safely control the vehicle 105. As part of theirimage processing, the deep convolutional neural network 173 andtaillight signal status determination module 175 can be configured towork with taillight signal analysis parameters 174, which can be used tocustomize and fine tune the operation of the taillight signalrecognition module 200. The taillight signal analysis parameters 174 canbe stored in a memory 172 of the in-vehicle control system 150.

In the example embodiment, the taillight signal recognition module 200can be configured to include an interface with the in-vehicle controlsystem 150, as shown in FIG. 1, through which the taillight signalrecognition module 200 can send and receive data as described herein.Additionally, the taillight signal recognition module 200 can beconfigured to include an interface with the in-vehicle control system150 and/or other ecosystem 101 subsystems through which the taillightsignal recognition module 200 can receive ancillary data from thevarious data sources described above. As described herein, the taillightsignal recognition module 200 can also be implemented in systems andplatforms that are not deployed in a vehicle and not necessarily used inor with a vehicle.

In an example embodiment as shown in FIG. 2, the taillight signalrecognition module 200 can be configured to include the deepconvolutional neural network 173 and taillight signal statusdetermination module 175, as well as other processing modules not shownfor clarity. Each of these modules can be implemented as software,firmware, or other logic components executing or activated within anexecutable environment of the taillight signal recognition system 201operating within or in data communication with the in-vehicle controlsystem 150. Each of these modules of an example embodiment is describedin more detail below in connection with the figures provided herein.

Systems and Methods for Detecting Taillight Signals of a Vehicle

A system and method for detecting taillight signals of a vehicle aredisclosed. In various example embodiments disclosed herein, a taillightsignal recognition system is provided. An example embodiment canautomatically detect taillight signals for all types of vehicles in realtime and in all driving conditions. The example embodiment can usefront-facing cameras mounted on the subject vehicle as input sensors.The example embodiments provide a method for automatically detectingtaillight signals of a proximate leading vehicle, which includesreceiving, at a computing device, a sequence of images from one or morecameras of a subject vehicle, generating a frame for each of the images,and labelling the images with one of three states of the taillightsignals of proximate leading vehicles. The method further includescreating a first and a second dataset corresponding to the images andtraining a convolutional neural network to combine the first and thesecond dataset. The method also includes identifying a confidence levelcorresponding to statistics of temporal patterns of taillight signals,loading the confidence level to a calculating model, and refiningparameters of the calculating model.

Referring again to FIG. 2, the diagram illustrates the components of thetaillight signal recognition module 200 of an example embodiment. Asdescribed in more detail below, the deep convolutional neural network173 and the taillight signal status determination module 175 serve toanalyze one or more input images 210 received from one or more of thevehicle sensor subsystems 144, including one or more cameras, anddetermine the status of taillight signals corresponding to the images ofproximate leading vehicles. The deep convolutional neural network 173and the taillight signal status determination module 175 can beconfigured to provide object detection of the taillight area in an imageinput 210 received from one or more of the vehicle sensor subsystems144, including one or more cameras. Alternatively, the deepconvolutional neural network 173 and the taillight signal statusdetermination module 175 can be configured to obtain a processed imagefrom an image processing module and/or a detection and tracking moduleof a subsystem the vehicle 105. In an example embodiment, the modules173 and 175 of the taillight signal recognition module 200 can receivethe one or more input images 210 and produce taillight signal statusinformation 220, as described in more detail below. The taillight signalstatus information 220 represents, for the image input 210, an outputand visualization of the vehicle definitions for each proximate leadingvehicle as determined by the object detection analysis of the imageinput 210. Additionally, the taillight signal status information 220represents an output and visualization of the taillight signal statusfor each proximate leading vehicle based on the processing operationsperformed by the taillight signal recognition module 200. As such, thetaillight signal status information 220 includes a current status of thedetected taillight signals for each instance of each leading vehicleobject detected in the one or more input images 210. The pixel-leveltaillight mask and instance-wise vehicle taillight status recognitionenables the taillight signal recognition module 200 to determine thetaillight status for each proximate leading vehicle, thereby enablingthe vehicle control subsystem to determine the intent of drivers inproximate leading vehicles. As a result, the autonomous controlsubsystem of the vehicle control subsystem 146 can more efficiently andsafely control the vehicle 105. As part of their image processing andtaillight status detection, the deep convolutional neural network 173and the taillight signal status determination module 175 can beconfigured to work with taillight signal analysis parameters 174, whichcan be used to customize and fine tune the operation of the taillightsignal recognition module 200. The taillight signal analysis parameters174 can be stored in a memory 172 of the in-vehicle control system 150.

Referring now to FIG. 3, a basic flow diagram illustrates an exampleembodiment of the system and method for detecting taillight signals of avehicle as disclosed herein. The basic operational flow as shown in FIG.3 is described below in combination with the sample images shown inFIGS. 4 through 6. FIGS. 4 through 6 illustrate an example of theprocessing performed by the taillight signal recognition module 200 ofan example embodiment. FIG. 4 illustrates a sample raw input imagereceived as image input 210. FIG. 5 illustrates an example of an objectdetection framework that uses bounding boxes to define the detection ofan object in the image input 210. In the example shown in FIG. 5, theinstances of vehicle objects detected in the raw image input 210 areeach framed with bounding boxes and given a unique identifier (ID). FIG.6 illustrates an example of the result of the processing performed bythe taillight signal recognition module 200 of an example embodiment,wherein a taillight signal has been recognized from one of the proximateleading vehicle objects detected in the raw image input 210. As part ofthe taillight signal status information 220 produced by the taillightsignal recognition module 200, an output and visualization of thetaillight signal status recognized for the proximate leading vehicle isproduced as shown for the vehicle identified as #2 in the example ofFIG. 6.

Referring again to FIG. 3 with reference to the example of FIGS. 4through 6 described above, a basic flow diagram illustrates an exampleembodiment of the system and method for detecting taillight signals of avehicle. In an example embodiment, the taillight recognition system 201of an example embodiment uses the images produced by one or morefront-facing cameras 305 as an input. In the example embodiment, thetaillight signal recognition module 200 can perform the object detectionprocess and application of the bounding boxes to define the detection ofobjects in the image input 210. In an alternative embodiment, thetaillight signal recognition module 200 can utilize the outputs producedby the detection and the tracking modules already provided in othervehicle subsystems. Essentially, the taillight signal recognition module200 can produce or receive the bounding boxes of all proximate leadingvehicles detected in every raw image input 210 frame from a detectionmodule. The taillight signal recognition module 200 can also determinean association of the bounding boxes across multiple frames in temporalsuccession (e.g., in a time-dependent sequence) and thereby track thesame detected vehicles over time using a tracking module. The trackingmodule can track the same detected vehicles over time (and over multipleimage frames) by assigning unique IDs to the bounding boxes of eachvehicle instance. This detection and tracking operation 310, as shown inFIG. 3, is an initial operation performed on the input images 210 by thetaillight signal recognition module 200.

In an example embodiment, a first step of taillight signal recognitionis single-frame taillight state classification as shown in operationblock 312 of FIG. 3. For each vehicle detected in each image frame, aneural network classifier can be applied to the image patches orportions corresponding to the detected vehicles by use of the deepconvolutional neural network 173. The neural network classifier canoutput two classification results for each detected vehicle regardingthe left and right parts of the taillight image portions, respectively.In an example embodiment, there are three possible taillight statusconditions for each of the two classification results: (1) theleft/right parts of the taillights are invisible (which could mean thetaillights are occluded by other objects or simply out of sight); (2)the left/right parts of the taillights are visible, but not illuminated;and (3) the left/right parts of the taillights are visible andilluminated. In an example embodiment, we use a specific architecture ofa convolutional neural network (CNN) called ResNet-18 for parts of thedeep convolutional neural network 173. The training of this neuralnetwork classifier is further described below. It will be apparent tothose of ordinary skill in the art in view of the disclosure herein thatother neural network architectures can similarly be used.

In the example embodiment, a second step of taillight signal recognitionis temporal state fusion as shown in operation block 314 of FIG. 3. Inthis operational step, we keep a long history of previous single-frametaillight status condition classification results for each vehicle fromthe neural network classifier as described above. The retained historyrepresents multiple image frames in temporal succession for each vehicleover a pre-configured or variable time period. The taillight signalrecognition module 200 can use a noise-resistant statistical inferencemethod to distinguish a brake light signal, a turn signal, and anemergency stop signal from the retained history of image frames. Thetaillight signal recognition module 200 can collect statistics of thetemporal patterns detected in the history of previous single-frametaillight status condition classification results. Over time, acollection of templates of the temporal state of the taillight imagescan be generated. The previous single-frame images can be matched to thecollection of templates to determine the status of the taillight signalsfor a particular vehicle. Once the status of the taillight signals for aparticular vehicle is determined, the taillight signal statusinformation 220 with corresponding outputs and visualizations can begenerated as shown in operation block 316 of FIG. 3. As described above,the taillight signal status information 220 represents, for the imageinput 210, an output and visualization of the pixel-level vehicledefinitions for each proximate leading vehicle and an output andvisualization of the taillight signal status for each proximate leadingvehicle based on the processing operations performed by the taillightsignal recognition module 200.

Regarding turn signal recognition specifically, the taillight signalrecognition module 200 can use two different inference outputs in anexample embodiment: one inference output is configured to respond faster(e.g., 100 milliseconds delay) but with a less certain result; the otherinference output is configured to respond more slowly (e.g., a 1 seconddelay) but with a more confident or more accurate result. It will beapparent to those of ordinary skill in the art in view of the disclosureherein that other implementations can use a greater or lesser number ofinference outputs. When accelerated by a graphics processing unit (GPU),the taillight recognition system of the example embodiment can run at 80Hz, exceeding the speed requirement for real-time processing.

Referring again to FIGS. 4 through 6, an example embodiment receives animage input 210. FIG. 4 illustrates a sample raw input image received asimage input 210. In an example embodiment and given the raw image input210, the deep convolutional neural network 173 of the taillight signalrecognition module 200 can use a deep convolutional neural network (CNN)to serve as a feature extraction module. FIG. 5 illustrates the resultof applying an object detection process to the raw image input 210 toproduce a feature map. The instances of vehicle objects detected in theraw image input 210 are each framed with bounding boxes and given aunique identifier (ID). The taillight signal status determination module175 of the taillight signal recognition module 200 can then apply ataillight recognition operation to recognize taillight signal status ofthe object instances detected in the image input 210. FIG. 6 illustratesan example of the result of the processing performed by the taillightsignal recognition module 200 of an example embodiment, wherein ataillight signal has been recognized from one of the proximate leadingvehicle objects detected in the raw image input 210. As part of thetaillight signal status information 220 produced by the taillight signalrecognition module 200, an output and visualization of the taillightsignal status recognized for the proximate leading vehicle is producedas shown for the vehicle identified as #2 in the example of FIG. 6.Thus, taillight signal recognition using a convolutional neural networkis disclosed.

Referring now to FIG. 7, a detailed flow diagram illustrates an exampleembodiment of a system and method for detecting taillight signals of avehicle. As shown in FIG. 7, the taillight recognition system 201 of anexample embodiment uses the images produced by one or more front-facingcameras 305 as an input. In the example embodiment, the taillight signalrecognition module 200 can perform the object detection process andapplication of the bounding boxes to define the detection of objects inthe image input 210. Alternatively, the taillight signal recognitionmodule 200 can utilize the outputs produced by the detection and thetracking modules already provided in other vehicle subsystems. Thetaillight signal recognition module 200 can produce or receive thebounding boxes of all proximate leading vehicles detected in every rawimage input 210 frame from a detection module. The taillight signalrecognition module 200 can also determine an association of the boundingboxes across frames in temporal succession and thereby track the samedetected vehicles over time using a tracking module. The tracking modulecan track the same detected vehicles over time (and over multiple imageframes) by assigning unique IDs to the bounding boxes of each vehicleinstance. This detection and tracking operation 310, as shown in FIG. 7,is an initial operation performed on the input images 210 by thetaillight signal recognition module 200.

Referring still to FIG. 7 in an example embodiment, a first step oftaillight signal recognition involves collecting a large dataset 410 ofthe images or image portions of vehicles' rear surfaces where taillightsare typically installed. We collect many test images and many hours oftest videos taken by front-facing cameras mounted on test vehicles. Weensure that a variety of roadway, weather, and lighting conditions arecovered. We include a variety of different types of vehicles in the testimages and videos. We also collect the outputs from the detection andthe tracking modules described above, so that the taillight signalrecognition module 200 can identify the rear surface appearances of eachvehicle in many image frames. These test images and videos along withthe outputs from the detection and the tracking modules can be used fortraining the deep convolutional neural network 173.

In an example embodiment, the training of the deep convolutional neuralnetwork 173 for single-frame taillight state classification can use twodifferent training datasets. The process used in an example embodimentfor building these two datasets is described next. The first type ofdataset is a classifier supervision dataset 420 for classifiersupervision. The classifier supervision dataset 420 contains pairs ofimage patches or portions of vehicle rear surfaces and theirclassifications. Each image patch or portion is classified, for the leftand right portion of the taillight separately, as one of the threetaillight status conditions: (1) the taillight is invisible, (2) thetaillight is visible but not illuminated, and (3) the taillight isvisible and illuminated. We use separate classifications for the leftand the right portions of the taillight because that is very useful fordetecting turn signals. In order to build the classifier supervisiondataset 420, we first sample image patches or portions from the generaldataset 410 collected in the first step as described above. In anexample embodiment, the image patches can be presented to a human imagelabeler for manual labelling. Because taillight signals can be uncommonin normal traffic, we use two sampling methods to sample image patchesor portions, so that the combined result has a balanced classdistribution. The first sampling method uses a uniformly random samplingfrom all vehicle bounding boxes in all image or video frames, whichyields only a few image patches or portions with illuminated taillights.The second sampling method uses positive-sample filtering, in which weuse simple taillight detectors to collect patches or portions withilluminated taillights. The simple taillight detector is imperfect, butgood enough for the sampling purpose. The results from the two samplingmethods are combined, yielding an image patch or portion collection thathas balanced class distribution. We can then present the image patchesor portions to a human labeler for manual labelling. As a result, theclassifier supervision dataset 420 can be generated from the convertedand combined labelling results and used for neural network training.

Referring still to FIG. 7 in the example embodiment, a second type ofdataset is a temporal smoothness dataset 422 collected to ensure thetemporal smoothness of the resulting neural network classifier. Thetemporal smoothness dataset 422 is useful, because we observe that theneural network classifier would otherwise become overly sensitive tosmall changes in the image patch or portion inputs, yieldinginconsistent prediction for a single vehicle within a fraction of asecond. This is not desirable for the temporal fusion process used inthe example embodiment and described above. The temporal smoothnessdataset 422 consists of pairs of image patches or portions of the samevehicle's rear surface, taken 100 milliseconds apart from each other.The temporal smoothness dataset 422 does not contain classificationresults, and thus requires no manual labelling. We uniformly randomlysample such image patch or portion pairs from the general dataset 410built in the first step, described above, to construct the temporalsmoothness dataset 422.

After generating the classifier supervision dataset 420 and the temporalsmoothness dataset 422 as described above, we can train the deepconvolutional neural network 173 using a common architecture calledResNet-18. We load the parameters of the neural network 173 from apre-trained model, and fine-tune the parameters to achieve an acceptablelevel of taillight signal recognition. We reduce the separateclassification task of the left and right taillight into one task byexploiting the left-right symmetry of the classification task. Flippingleft-right the image patch or portion would convert one classificationtask to the other, so we only need to train one classifier for bothtasks. The training of the deep convolutional neural network 173 can beaccomplished using the datasets 420 and 422 and the processes describedabove. After we have finished training the single-frame taillight stateclassifier as detailed above, we can collect some characteristicstatistics for taillight temporal fusion. Among many statistics, animportant statistic is one corresponding to the patterns of turn signalsand emergency stop signals of various vehicles. We use those statisticsto filter noisy predictions from the single-frame classifier, and makeconfident predictions by integrating temporal information in thetemporal fusion process as described above.

The example embodiments can use the output produced by the vehicledetection and tracking module of an autonomous vehicle control system.The example embodiment can also run on the on-board computer equippedwith a graphics processing unit (GPU). In the various embodimentsdescribed herein, the taillight signal recognition module 200 canproduce taillight signal status information 220 representing thetaillight states of all vehicles in sight up to 100 meters (328 feet)away at a frequency of over 80 Hz. Thus, taillight signal recognitionusing a convolutional neural network is disclosed.

Systems and Methods for Vehicle Taillight State Recognition

When driving on a roadway, the taillight illumination status of front orleading vehicles, as well as other proximate vehicles, is a strongvisual sign indicating the likely behavior of the proximate vehicles atthe current time or in the near future. For example, the taillightillumination status of proximate vehicles can indicate current orimminent behaviors, such as braking, turning, emergency response(flashing), or even reversing. After the taillight illumination statusof a proximate vehicle is effectively recognized, more efficient andsafer autonomous vehicle control actions and motion planning can beaccomplished. Additionally, better trajectory prediction and speedestimation of other proximate vehicles and can be achieved. Theseefficiencies result in a safer, more reliable, and more robustautonomous vehicle driving system. As described in more detail below forexample embodiments, a taillight signal recognition system is disclosedfor use on or with autonomous vehicles or in driving environmentsimulation.

The taillight signal recognition system of the example embodiments canbe implemented by generating datasets and machine learning models torecognize and act upon the taillight illumination status of proximatevehicles. In particular, an example embodiment can be implemented by: 1)creating a trajectory level fully human-annotated dataset for taillightstate recognition; 2) creating a deep learning based feature extractorfor taillight mask feature extraction; and 3) creating a machinelearning based model for accurate trajectory level taillight staterecognition. The creation and use of these datasets and machine learningmodels for example embodiments are described in more detail below.

In an example embodiment, the taillight signal recognition system isconfigured to create three separate human-annotated datasets for thedisclosed taillight recognition system. These datasets, as shown in FIG.8, can include: 1) a single-frame taillight luminance or illuminationstatus dataset 520, 2) a single-frame taillight mask dataset 521, and 3)a multi-frame, trajectory level taillight luminance or illuminationstatus dataset 522. Each of these datasets are described in detailbelow.

Referring still to FIG. 8, the single-frame taillight luminance orillumination status dataset 520 can be generated in the same manner asclassifier supervision dataset 420 described above. In particular, foreach proximate vehicle, the taillight signal recognition system can beconfigured to annotate the illumination status of the taillights of eachproximate vehicle for each single frame of the plurality of images(e.g., general dataset 410 described above) captured from theenvironment around the autonomous vehicle 105. The taillightillumination status of each proximate vehicle can include statusindications, such as (1) the taillight is invisible or occluded, (2) thetaillight is visible but not illuminated or dark, (3) the taillight isvisible and illuminated or bright, and (4) the taillight status isunknown. The taillight illumination status of each proximate vehicle canalso include inferred status indications, such as brake (e.g., theproximate vehicle is braking), right turn on (e.g., the proximatevehicle is signaling a right turn), left turn on (e.g., the proximatevehicle is signaling a left turn), hazard on (e.g., the proximatevehicle is signaling an emergency or hazard condition), etc. Theseillumination status indications and inferred status indications orannotations can be retained in dataset 520 for the left and righttaillights of each proximate vehicle in each single image frame.

The datasets used in an example embodiment as shown in FIG. 8 can alsoinclude single-frame taillight mask dataset 521. Because the taillightillumination status of any particular vehicle only depends on thefeatures within a predefined taillight region for each vehicle, theaccurate acquisition of the taillight region for each vehicle is highlydesired. This predefined taillight region for each vehicle can beisolated in a taillight mask annotation. In the example embodiment, thetaillight signal recognition system is configured to annotate the lefttaillight region and right taillight region of each proximate vehicleusing a mask annotation as shown by the example in FIG. 9.

FIG. 9 illustrates an example of a taillight mask annotation for aparticular proximate vehicle. The proximate vehicle in the example imageof FIG. 9 is shown in a green rectangular bounding box 910. The leftpredefined taillight region 920 and the right predefined taillightregion 930 are annotated with a green mask and a yellow mask,respectively. The predefined taillight region for other proximatevehicles in each single frame of the plurality of images from generaldataset 410 can be similarly annotated with a single-frame taillightmask. The single-frame taillight mask annotations can be retained indataset 521.

Referring again to FIG. 8, the datasets used in an example embodimentcan include a multi-frame, trajectory level taillight luminance orillumination status dataset 522. Because the accurate taillightillumination state (e.g., braking, turning, flashing, etc.) istime-dependent, it is not sufficient to predict the taillightillumination state given only a single image frame. Therefore, thetaillight signal recognition system disclosed herein is configured totake a series of image frames (multiple frames) of trajectory trackedproximate vehicles into consideration. The series of image frames ofeach proximate vehicle represents multiple image frames of the proximatevehicle in temporal succession. The taillight signal recognition systemis configured to annotate the taillight illumination state for each ofthe proximate vehicles in each individual frame of a multiple imageframe set in temporal succession. In a particular example embodiment,the multi-frame taillight illumination status annotations can beimplemented as the data generated for the temporal smoothness dataset422 described above. The generation and use of the multi-frame taillightillumination status annotations improves the accuracy of the predictedbehavior of the proximate vehicles and improves autonomous vehiclecontrol and motion planning. The multi-frame, trajectory level taillightluminance or illumination status annotations can be retained in dataset522.

Once the datasets 520, 521, and 522 are created as described above, thetaillight signal recognition system of an example embodiment isconfigured to perform three basic operations to recognize and act uponthe taillight illumination status of proximate vehicles. These threebasic operations in an example embodiment can include, 1) featureextraction, 2) feature aggregation, and 3) prediction. These operationscan be performed by the taillight signal status determination module 175of the taillight signal recognition system 201 as described above andconfigured in the manner described herein. Each of these operations areillustrated in FIG. 10 and described in detail below.

Feature Extraction

The single-frame taillight illumination status dataset 520 can be usedwith supervised signals to train the deep convolutional neural network173 as shown in FIG. 2. The trained deep convolutional neural network173 can then be used to process raw image input from cameras of anautonomous vehicle. This raw image input can be received as image input210 as shown in FIG. 2. In an example embodiment and given the raw imageinput 210, the trained deep convolutional neural network 173 of thetaillight signal recognition module 200 can serve as a featureextraction module by applying feature extraction and/or an objectdetection process to the raw image input 210 to produce a feature map.The instances of vehicle objects detected in the raw image input 210 areeach framed with bounding boxes and given a unique identifier (ID). Thetaillight signal status determination module 175 and the trained deepconvolutional neural network 173 of the taillight signal recognitionmodule 200 can then apply a taillight signal status recognitionoperation to predict or recognize taillight signal status of the objectinstances detected in the image input 210. For example, the taillightsignal status recognition operation, as shown in FIG. 9, includes asingle-frame level recognition of left and right taillight illuminationstatus and generation of the left and right taillight mask for aproximate vehicle identified in image input 210.

Referring to FIG. 10, the image frames of the image input 210 arereceived for feature extraction as described above. The taillight signalstatus determination module 175 and the trained deep convolutionalneural network 173 of the taillight signal recognition module 200 canapply the taillight signal status recognition operation to extractfeatures from single image frames and recognize taillight signal statusof the object instances detected in the single image frames of the imageinput 210. The taillight signal status determination module 175 and thetrained deep convolutional neural network 173 of the taillight signalrecognition module 200 can also generate the left and right taillightmask for each proximate vehicle identified in the image input 210. Afterthe taillight mask is generated, local feature extraction can also beperformed on each side of the taillight mask to extract additionalfeatures, including but not limited to, color, histograms, localdescriptors, motion, and flow.

Feature Aggregation

Having performed the single-frame image feature extraction as describedabove, it is also important to process multiple image frames in temporalsuccession to capture time-dependent temporal features among theextracted single-frame features. For this purpose, the taillight signalrecognition system of the example embodiments applies a featureaggregation operation after the feature extraction operation as shown inFIG. 10. Feature aggregation enables the taillight signal recognitionsystem to capture the variation of the taillight illumination state inthe proximate vehicle trajectory over time and over a temporalsuccession of image frames. In an example embodiment, the taillightsignal recognition system implements the feature aggregation operationby integrating each extracted feature and its corresponding featurevalue with its feature counterpart in each previous image frame within apredefined time window. The feature integration methods can include, butare not limited to, subtraction, addition, and learning based models(e.g., Hidden Markov Models—HMM, graphical models, etc.). Integratingall these feature values for the same extracted feature over multipleimage frames in temporal succession yields the full representation ofthe extracted and aggregated features. The aggregated feature data canbe retained in the multi-frame, trajectory level taillight luminance orillumination status dataset 522.

Prediction

As described above, the taillight signal status determination module 175and the trained deep convolutional neural network 173 of the taillightsignal recognition module 200 can generate aggregated feature data usingthe disclosed feature extraction operation and the feature aggregationoperation. As a result, the aggregated feature data represents featurevalues for extracted features over multiple image frames in temporalsuccession. In particular, the aggregated feature data can represent theillumination state of taillights of proximate vehicles over apre-defined time window. This aggregated feature data enables thetaillight signal recognition module 200 to predict or recognize theillumination state of taillights of proximate vehicles near theautonomous vehicle 105 over the pre-defined time window and over apre-defined number of image frames in temporal succession.

For example, FIG. 11 illustrates a multiple image frame set in temporalsuccession, with each image frame numbered 1 to 10. Each image frame ofthe set corresponds to an image of a rear portion of a proximate vehiclecaptured in the image input 210. As described above, the taillightsignal recognition moule 200 can use the taillight signal statusdetermination module 175 and the trained deep convolutional neuralnetwork 173 to isolate the taillight portion of the rear image of theproximate vehicle with the single-frame taillight mask dataset 521. Thetaillight signal status determination module 175 and the trained deepconvolutional neural network 173 can also use the single-frame taillightluminance or illumination status dataset 520 to determine the taillightillumination state of the vehicle taillights at a moment in time.Additionally, as described above, the taillight signal recognitionmodule 200 can also aggregate the taillight feature data from each imageover multiple image frames and over the pre-determined time window toproduce a more accurate taillight illumination status prediction. Asshown in FIG. 11, the multiple image frames of the tracked proximatevehicle in temporal succession are received by the taillight signalrecognition module 200 and labeled with a frame identifier (1 to 10).For each image frame, the taillight signal recognition system can beconfigured to predict or recognize the taillight illumination state. Forexample in FIG. 11, the taillight illumination state for image frame 3to image frame 10 is correctly predicted or recognized by the taillightsignal recognition module 200 as “brake” (e.g., the proximate vehicle isbraking). The recognized taillight illumination state for each proximatevehicle can be output by the taillight signal recognition system astaillight signal status information 220.

In another example shown in FIG. 12, the multiple image frames of thetracked proximate vehicle in temporal succession are received by thetaillight signal recognition module 200 and labeled with a frameidentifier (1 to 10). For each image frame, the taillight signalrecognition system can be configured to predict or recognize thetaillight illumination state. For example in FIG. 12, the taillightillumination state for image frames 1 to 10 is correctly predicted orrecognized by the taillight signal recognition module 200 as “brake”(e.g., the proximate vehicle is braking). Additionally, the taillightillumination state for image frames 1 to 3 and 9 to 10 is correctlypredicted or recognized by the taillight signal recognition module 200as “right turn” (e.g., the proximate vehicle is signaling a right turn).Again, the recognized taillight illumination state for each proximatevehicle can be output by the taillight signal recognition system astaillight signal status information 220.

Once the taillight signal recognition system has produced the taillightsignal status information 220 as described above, the vehicle subsystems140 can use the taillight signal status information 220 to modify thecontrol actions, the trajectory, and/or the route planning for theautonomous vehicle 105 accordingly. For example, if the taillight signalrecognition system determines that a proximate or leading vehicle isbraking based on the illumination state of the leading vehicle'staillights, the vehicle subsystems 140 of the autonomous vehicle 105 canbe commanded to slow or brake the autonomous vehicle 105 incorresponding fashion. For another example, if the taillight signalrecognition system determines that a proximate or leading vehicle issignaling a right or left turn, based on the illumination state of theleading vehicle's taillights, the vehicle subsystems 140 of theautonomous vehicle 105 can be commanded to modify the trajectory and/orspeed of the autonomous vehicle 105 in corresponding fashion. Thus, thetaillight signal status information 220 produced by the taillight signalrecognition system as described above can be used to modify the controlsignals and route planning for an autonomous vehicle.

Referring now to FIG. 13, a flow diagram illustrates an exampleembodiment of a system and method 1000 for taillight signal recognition.The example embodiment can be configured for: receiving a plurality ofimage frames from one or more image-generating devices of an autonomousvehicle (processing block 1010); using a single-frame taillightillumination status annotation dataset and a single-frame taillight maskdataset to recognize a taillight illumination status of a proximatevehicle identified in an image frame of the plurality of image frames,the single-frame taillight illumination status annotation datasetincluding one or more taillight illumination status conditions of aright or left vehicle taillight signal, the single-frame taillight maskdataset including annotations to isolate a taillight region of a vehicle(processing block 1020); and using a multi-frame taillight illuminationstatus dataset to recognize a taillight illumination status of theproximate vehicle in multiple image frames of the plurality of imageframes, the multiple image frames being in temporal succession(processing block 1030).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the in-vehicle control system 150, the taillight signal recognitionmodule 200, and/or the taillight signal recognition module 200 asdescribed herein to obtain read or write access to data signals,messages, or content communicated via any mode of data communications.In many cases, the mobile device 130 is a handheld, portable device,such as a smart phone, mobile phone, cellular telephone, tabletcomputer, laptop computer, display pager, radio frequency (RF) device,infrared (IR) device, global positioning device (GPS), Personal DigitalAssistants (PDA), handheld computers, wearable computer, portable gameconsole, other mobile communication and/or computing device, or anintegrated device combining one or more of the preceding devices, andthe like. Additionally, the mobile device 130 can be a computing device,personal computer (PC), multiprocessor system, microprocessor-based orprogrammable consumer electronic device, network PC, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, and the like, and is not limited to portable devices. Themobile device 130 can receive and process data in any of a variety ofdata formats. The data format may include or be configured to operatewith any programming format, protocol, or language including, but notlimited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the in-vehicle control system 150, the taillight signal recognitionmodule 200, and/or the taillight signal recognition module 200 asdescribed herein to obtain read or write access to data signals,messages, or content communicated via any mode of inter-process ornetworked data communications. In many cases, the network resource 122is a data network accessible computing platform, including client orserver computers, websites, mobile devices, peer-to-peer (P2P) networknodes, and the like. Additionally, the network resource 122 can be a webappliance, a network router, switch, bridge, gateway, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” can also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Thenetwork resources 122 may include any of a variety of providers orprocessors of network transportable digital content. Typically, the fileformat that is employed is Extensible Markup Language (XML), however,the various embodiments are not so limited, and other file formats maybe used. For example, data formats other than Hypertext Markup Language(HTML)/XML or formats other than open/standard data formats can besupported by various embodiments. Any electronic file format, such asPortable Document Format (PDF), audio (e.g., Motion Picture ExpertsGroup Audio Layer 3—MP3, and the like), video (e.g., MP4, and the like),and any proprietary interchange format defined by specific content sitescan be supported by the various embodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the in-vehicle control system 150, the taillight signal recognitionmodule 200, and/or the taillight signal recognition module 200 tointeract with one or more components of a vehicle subsystem. Theseclient devices 132 or 122 may include virtually any computing devicethat is configured to send and receive information over a network, suchas network 120 as described herein. Such client devices may includemobile devices, such as cellular telephones, smart phones, tabletcomputers, display pagers, radio frequency (RF) devices, infrared (IR)devices, global positioning devices (GPS), Personal Digital Assistants(PDAs), handheld computers, wearable computers, game consoles,integrated devices combining one or more of the preceding devices, andthe like. The client devices may also include other computing devices,such as personal computers (PCs), multiprocessor systems,microprocessor-based or programmable consumer electronics, network PC's,and the like. As such, client devices may range widely in terms ofcapabilities and features. For example, a client device configured as acell phone may have a numeric keypad and a few lines of monochrome LCDdisplay on which only text may be displayed. In another example, aweb-enabled client device may have a touch sensitive screen, a stylus,and a color LCD display screen in which both text and graphics may bedisplayed. Moreover, the web-enabled client device may include a browserapplication enabled to receive and to send wireless application protocolmessages (WAP), and/or wired application messages, and the like. In oneembodiment, the browser application is enabled to employ HyperTextMarkup Language (HTML), Dynamic HTML, Handheld Device Markup Language(HDML), Wireless Markup Language (WML), WMLScript, JavaScript™,EXtensible HTML (xHTML), Compact HTML (CHTML), and the like, to displayand send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The in-vehicle control system 150, the taillight signal recognitionmodule 200, and/or the taillight signal recognition module 200 can beimplemented using systems that enhance the security of the executionenvironment, thereby improving security and reducing the possibilitythat the in-vehicle control system 150, the taillight signal recognitionmodule 200, and/or the taillight signal recognition module 200 and therelated services could be compromised by viruses or malware. Forexample, the in-vehicle control system 150, the taillight signalrecognition module 200, and/or the taillight signal recognition module200 can be implemented using a Trusted Execution Environment, which canensure that sensitive data is stored, processed, and communicated in asecure way.

FIG. 14 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth©, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

What is claimed is:
 1. A method comprising: receiving a plurality ofimage frames from an image-generating device of an autonomous vehicle;performing feature extraction and object detection for at least oneimage frame of the plurality of image frames to detect a proximatevehicle object in the image frame of the plurality of image frames;using a taillight illumination status annotation dataset to recognize ataillight illumination status of the proximate vehicle identified in theimage frame of the plurality of image frames, the taillight illuminationstatus annotation dataset comprising one or more taillight illuminationstatus according to a right or left vehicle taillight signal; using ataillight mask dataset with annotations to isolate a taillight region ofthe proximate vehicle, the taillight region being a portion of theproximate vehicle object detected by feature extraction and objectdetection; integrating a feature extracted from the isolated taillightregion with a corresponding feature extracted from the isolatedtaillight region in a previous image frame within a predefined timewindow; determining an intention of the proximate vehicle based on thetaillight illumination status; and performing an autonomous vehiclecontrol signal or route planning modification based on the taillightillumination status of the proximate vehicle.
 2. The method of claim 1further comprising training a deep convolutional neural network with thetaillight illumination status annotation dataset and using the deepconvolutional neural network to perform a feature extraction on theplurality of image frames.
 3. The method of claim 2 wherein the featureextraction is performed using an object detection process andapplication of bounding boxes to define a detection of objects in theimage frame.
 4. The method of claim 1 wherein the taillight mask datasetincludes annotations to isolate a left or right taillight region of theproximate vehicle.
 5. The method of claim 4 comprising performing alocal feature extraction on each side of a taillight mask of thetaillight mask dataset.
 6. The method of claim 1 further comprisingperforming a feature aggregation.
 7. The method of claim 1 whereinintegrating an extracted feature comprises integrating a taillightsignal status with the extracted feature.
 8. The method of claim 1further comprising: sending a command to an autonomous control subsystemof the autonomous vehicle, the command corresponding to the intention ofthe proximate vehicle.
 9. The method of claim 1, wherein themodification includes commanding the autonomous vehicle to slow or brakein response to a taillight illumination status of the proximate vehicleindicative of a braking action.
 10. A system comprising: a dataprocessor; and a taillight signal recognition module, executable by thedata processor, the taillight signal recognition module being configuredto perform a taillight signal recognition operation, the taillightsignal recognition operation being configured to: receive a plurality ofimage frames from an image-generating device of an autonomous vehicle;perform feature extraction and object detection for at least one imageframe of the plurality of image frames to detect a proximate vehicleobject in the image frame of the plurality of image frames; use ataillight illumination status annotation dataset to recognize ataillight illumination status of a proximate vehicle identified in animage frame of the plurality of image frames, the taillight illuminationstatus annotation dataset comprising one or more taillight illuminationstatus according to a right or left vehicle taillight signal; use ataillight mask dataset with annotations to isolate a taillight region ofthe proximate vehicle; integrate a feature extracted from the isolatedtaillight region with a corresponding feature extracted from theisolated taillight region in a previous image frame within a predefinedtime window; determine an intention of the proximate vehicle based onthe taillight illumination status; and perform an autonomous vehiclecontrol signal or route planning modification based on the taillightillumination status of the proximate vehicle.
 11. The system of claim 10wherein the taillight illumination status comprises an indication fromthe group consisting of: the taillight is invisible or occluded, thetaillight is visible but not illuminated or dark, the taillight isvisible and illuminated or bright, and the taillight status is unknown.12. The system of claim 10 wherein the taillight illumination statuscomprises an inferred indication from the group consisting of: brake,right turn on, left turn on, and hazard on.
 13. The system of claim 10wherein the taillight signal recognition operation is further configuredto: train a deep convolutional neural network with the taillightillumination status annotation dataset and use the deep convolutionalneural network to perform a feature extraction on the plurality of imageframes, wherein the deep convolutional neural network is trained usingdatasets comprising a classifier supervision dataset and a temporalsmoothness dataset.
 14. The system of claim 10 wherein a taillight maskdataset is used to recognize the taillight illumination status of theproximate vehicle, the taillight mask dataset comprising annotations toisolate a left taillight region and a right taillight region of theproximate vehicle, wherein a local feature extraction is performed on atleast one of the left taillight region and the right taillight region.15. The system of claim 10 wherein the taillight signal recognitionoperation is further configured to: perform a feature aggregation forthe plurality of the image frames, wherein the feature aggregation isfurther configured to integrate an extracted feature in a first imageframe, with a feature counterpart in a second image frame prior to thefirst image frame, within a predefined time window.
 16. The system ofclaim 15 wherein an integration of the extracted feature and the featurecounterpart is conducted using a subtraction model, an addition model,or a learning-based model.
 17. The system of claim 10 wherein thetaillight signal recognition operation is further configured to: use amulti-frame taillight illumination status dataset to recognize ataillight illumination status of the proximate vehicle in multiple imageframes of the plurality of image frames, the multiple image frames beingin temporal succession.
 18. The system of claim 10 wherein the intentionof the proximate vehicle comprises brake, right turn, and left turn. 19.A non-transitory machine-useable storage medium embodying instructionswhich, when executed by a machine, cause the machine to: receive aplurality of image frames from an image-generating device of anautonomous vehicle; perform feature extraction and object detection forat least one image frame of the plurality of image frames to detect aproximate vehicle object in the image frame of the plurality of imageframes; use a taillight illumination status annotation dataset torecognize a taillight illumination status of a proximate vehicleidentified in an image frame of the plurality of image frames, thetaillight illumination status annotation dataset comprising one or moretaillight illumination status according to a right or left vehicletaillight signal; use a taillight mask dataset with annotations toisolate a taillight region of the proximate vehicle; integrate a featureextracted from the isolated taillight region with a correspondingfeature extracted from the isolated taillight region in a previous imageframe within a predefined time window; determine an intention of theproximate vehicle based on the taillight illumination status; andperform an autonomous vehicle control signal or route planningmodification based on the taillight illumination status of the proximatevehicle.
 20. The non-transitory machine-useable storage medium of claim19 being further configured to: use a deep convolutional neural networkto perform a feature extraction on the plurality of image frames, thefeature extraction being performed using an object detection process andapplication of bounding boxes to define a detection of objects, whereineach of the objects is given a unique identifier.